ALIF EXPLORATION
Selected Variables
base: Code of the patient
covariates:
- Age
- Gender
- Prior Spine Surgery
- '1st surgeon: experience in ASD surgery'
- ASA classification
- Decompression
- Osteotomy
- 3CO
- SPOs
- BMI_First Visit
- Tobacco use_First Visit
- Osteoporosis / osteopenia
- Previous surgery - LEV
- LGap
- RLL
- Cobb LS curve (Degree)
- Number of Interbody Fusions
- 'Posterior Instrumented Fusion: Upper / Lower Levels'
- Alif
- LL-Lordosis Difference
outcomes_ql:
- 2Y. ODI - Score (%)
- 2Y. SRS22 - SRS Subtotal score
- 2Y. SF36 - MCS
- 2Y. SF36 - PCS
outcomes_radiology:
- 6W. Major curve Cobb angle
- 1Y. Major curve Cobb angle
- 2Y. Major curve Cobb angle
- 6W. T1 Sagittal Tilt
- 1Y. T1 Sagittal Tilt
- 2Y. T1 Sagittal Tilt
- 6W. Sagittal Balance
- 1Y. Sagittal Balance
- 2Y. Sagittal Balance
- 6W. Global Tilt
- 1Y. Global Tilt
- 2Y. Global Tilt
- 6W. Lordosis (top of L1-S1)
- 1Y. Lordosis (top of L1-S1)
- 2Y. Lordosis (top of L1-S1)
- 6W. LGap
- 1Y. LGap
- 2Y. LGap
- 6W. Pelvic Tilt
- 1Y. Pelvic Tilt
- 2Y. Pelvic Tilt
predictive:
- Weight (kgs)_First Visit
- Height (cm)_First Visit
- Total surgical time st1+st2+st3
- Osteotomy
- Alcohol/drug abuse
- Anemia or other blood disorders
- Osteoarthritis
- Mild vascular
- Depression / anxiety
- Diabetes with end organ damage
- Cardiac
- Hypertension
- Chronic pulmonary disease
- Nervous system disorders
- Renal
- Peripheral vascular disease
- Psychiatric / Behavioral
- Peptic ulcer
- Bladder incontinence
- Bowel incontinence
- Leg weakness
- Loss of balance
- NRS back - Leg pain - Average
- Tobacco use_First Visit
- Years with spine problems
- ODI - Score (%)_First Visit
- SRS22 - SRS Total score_First Visit
- SF36 - PCS_First Visit
- SF36 - MCS_First Visit
- Major curve Cobb angle
demographic:
- Age
- Gender
- Prior Spine Surgery
- ASA classification
- 3CO
- BMI_First Visit
- Global Tilt
- ideal LL
- Lordosis (top of L1-S1)
- ODI - Score (%)_First Visit
- SRS22 - SRS Total score_First Visit
- SF36 - PCS_First Visit
- SF36 - MCS_First Visit
- Major curve Cobb angle
expanded:
- Age
- Gender
- Prior Spine Surgery
- '1st surgeon: experience in ASD surgery'
- ASA classification
- Decompression
- Osteotomy
- 3CO
- SPOs
- BMI_First Visit
- Tobacco use_First Visit
- Osteoporosis / osteopenia
- Previous surgery - LEV
- LGap
- RLL
- Cobb LS curve (Degree)
- Number of Interbody Fusions
- 'Posterior Instrumented Fusion: Upper / Lower Levels'
- Alif
- LL-Lordosis Difference
- Weight (kgs)_First Visit
- Height (cm)_First Visit
- Total surgical time st1+st2+st3
- Alcohol/drug abuse
- Anemia or other blood disorders
- Osteoarthritis
- Mild vascular
- Depression / anxiety
- Diabetes with end organ damage
- Cardiac
- Hypertension
- Chronic pulmonary disease
- Nervous system disorders
- Renal
- Peripheral vascular disease
- Psychiatric / Behavioral
- Peptic ulcer
- Bladder incontinence
- Bowel incontinence
- Leg weakness
- Loss of balance
- NRS back - Leg pain - Average
- Years with spine problems
- ODI - Score (%)_First Visit
- SRS22 - SRS Total score_First Visit
- SF36 - PCS_First Visit
- SF36 - MCS_First Visit
- Major curve Cobb angle
- SRS22 - SRS Subtotal score_First Visit
- T1 Sagittal Tilt
- Sagittal Balance
- Global Tilt
- Lordosis (top of L1-S1)
- Pelvic TiltPropensity Scores Common Support
Model Stats
- Treatment proportion: 0.127
- Model Type: elastic_net
- Accuracy: 0.8967527
- Params: alpha: 0.1692308 lambda: 0.0113789
Average Treatment Effects - Radiology
Outcome: 6W. Major curve Cobb angle
Distribution:
0% 25% 50% 75% 100%
-65.000 -21.000 -10.440 -3.415 16.880
Model Type Y: boosting
RMSE: 18.9801525018218
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.5
Model Type No: boosting
RMSE: 13.0923940700935
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0
ATE (Yes-No): -6.115 (Std.Error: 4.612)
Trimmed ATE (Yes-No): -5.935 (Std.Error: 4.81)
Upper ATE (Yes-No): -10.493 (Std.Error: 4.909)
Observational differences in treatment 1.867 (Yes-No)
treatment outcome
1: Yes 22.89611
2: No 21.02903`geom_smooth()` using method = 'loess' and formula 'y ~ x'
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`geom_smooth()` using formula 'y ~ x'
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`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
Outcome: 1Y. Major curve Cobb angle
Distribution:
0% 25% 50% 75% 100%
-64.00 -22.97 -9.93 -2.28 22.44
Model Type Y: boosting
RMSE: 18.974074896311
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0
Model Type No: boosting
RMSE: 14.4303807391454
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.75
ATE (Yes-No): 1.112 (Std.Error: 3.512)
Trimmed ATE (Yes-No): 1.471 (Std.Error: 3.639)
Upper ATE (Yes-No): -7.204 (Std.Error: 5.159)
Observational differences in treatment 3.198 (Yes-No)
treatment outcome
1: Yes 23.67687
2: No 20.47880`geom_smooth()` using method = 'loess' and formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
Outcome: 2Y. Major curve Cobb angle
Distribution:
0% 25% 50% 75% 100%
-60.00 -23.52 -9.53 -1.08 21.99
Model Type Y: boosting
RMSE: 20.6117331907075
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0
Model Type No: boosting
RMSE: 14.4386975956021
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.625
ATE (Yes-No): -4.364 (Std.Error: 4.921)
Trimmed ATE (Yes-No): -4.307 (Std.Error: 5.155)
Upper ATE (Yes-No): -5.469 (Std.Error: 6.805)
Observational differences in treatment 1.849 (Yes-No)
treatment outcome
1: Yes 23.97219
2: No 22.12335`geom_smooth()` using method = 'loess' and formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
Outcome: 6W. T1 Sagittal Tilt
Distribution:
0% 25% 50% 75% 100%
-23.631420 -5.244884 -1.457698 2.000000 18.000000
Model Type Y: boosting
RMSE: 6.74157142188607
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.75
Model Type No: boosting
RMSE: 5.71961223043204
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0
ATE (Yes-No): -6.907 (Std.Error: 1.402)
Trimmed ATE (Yes-No): -7.043 (Std.Error: 1.488)
Upper ATE (Yes-No): -3.445 (Std.Error: 3.773)
Observational differences in treatment -0.932 (Yes-No)
treatment outcome
1: Yes -3.688515
2: No -2.756435`geom_smooth()` using method = 'loess' and formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
Outcome: 1Y. T1 Sagittal Tilt
Distribution:
0% 25% 50% 75% 100%
-30.098675 -5.534988 -2.000000 1.480410 20.000000
Model Type Y: boosting
RMSE: 6.37495044526522
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.625
Model Type No: boosting
RMSE: 5.98466020988
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.875
ATE (Yes-No): -4.179 (Std.Error: 1.587)
Trimmed ATE (Yes-No): -4.094 (Std.Error: 1.669)
Upper ATE (Yes-No): -5.797 (Std.Error: 3.065)
Observational differences in treatment 0.143 (Yes-No)
treatment outcome
1: Yes -2.502714
2: No -2.645953`geom_smooth()` using method = 'loess' and formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
Outcome: 2Y. T1 Sagittal Tilt
Distribution:
0% 25% 50% 75% 100%
-31.332362 -5.685001 -1.366539 1.078189 10.268933
Model Type Y: boosting
RMSE: 7.95627929697417
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.875
Model Type No: boosting
RMSE: 5.51415499755095
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0
ATE (Yes-No): -5.817 (Std.Error: 1.231)
Trimmed ATE (Yes-No): -5.821 (Std.Error: 1.292)
Upper ATE (Yes-No): -5.729 (Std.Error: 3.456)
Observational differences in treatment -0.997 (Yes-No)
treatment outcome
1: Yes -3.676968
2: No -2.680294`geom_smooth()` using method = 'loess' and formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
Outcome: 6W. Sagittal Balance
Distribution:
0% 25% 50% 75% 100%
-194.79 -68.55 -27.01 2.10 114.15
Model Type Y: boosting
RMSE: 48.8982406338102
Params: nrounds: 150.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.5
Model Type No: boosting
RMSE: 51.7032596483081
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0
ATE (Yes-No): -55.668 (Std.Error: 12.94)
Trimmed ATE (Yes-No): -56.805 (Std.Error: 13.222)
Upper ATE (Yes-No): -32.826 (Std.Error: 26.464)
Observational differences in treatment -8.982 (Yes-No)
treatment outcome
1: Yes 23.00886
2: No 31.99110`geom_smooth()` using method = 'loess' and formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
Outcome: 1Y. Sagittal Balance
Distribution:
0% 25% 50% 75% 100%
-237.47 -62.47 -28.13 7.24 109.54
Model Type Y: boosting
RMSE: 49.8567669557646
Params: nrounds: 100.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.625
Model Type No: boosting
RMSE: 52.5469573981032
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0
ATE (Yes-No): -50.077 (Std.Error: 9.846)
Trimmed ATE (Yes-No): -49.618 (Std.Error: 10.668)
Upper ATE (Yes-No): -58.827 (Std.Error: 26.621)
Observational differences in treatment -3.004 (Yes-No)
treatment outcome
1: Yes 34.28567
2: No 37.29009`geom_smooth()` using method = 'loess' and formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
Outcome: 2Y. Sagittal Balance
Distribution:
0% 25% 50% 75% 100%
-252.690 -56.225 -17.085 7.660 107.700
Model Type Y: boosting
RMSE: 75.9737308698098
Params: nrounds: 100.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.5
Model Type No: boosting
RMSE: 52.0131451876266
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0
ATE (Yes-No): -61.063 (Std.Error: 7.764)
Trimmed ATE (Yes-No): -60.308 (Std.Error: 8.069)
Upper ATE (Yes-No): -78.92 (Std.Error: 38.203)
Observational differences in treatment -14.719 (Yes-No)
treatment outcome
1: Yes 26.24857
2: No 40.96763`geom_smooth()` using method = 'loess' and formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
Outcome: 6W. Global Tilt
Distribution:
0% 25% 50% 75% 100%
-68.6200 -18.0175 -6.0000 1.8425 16.0000
Model Type Y: boosting
RMSE: 14.7995628723697
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0
Model Type No: boosting
RMSE: 12.1017197310102
Params: nrounds: 50.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.5
ATE (Yes-No): -15.214 (Std.Error: 4.226)
Trimmed ATE (Yes-No): -15.378 (Std.Error: 4.335)
Upper ATE (Yes-No): -11.443 (Std.Error: 5.213)
Observational differences in treatment -5.812 (Yes-No)
treatment outcome
1: Yes 18.04944
2: No 23.86111`geom_smooth()` using method = 'loess' and formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
Outcome: 1Y. Global Tilt
Distribution:
0% 25% 50% 75% 100%
-62.630 -15.715 -5.100 1.000 26.000
Model Type Y: boosting
RMSE: 15.8269203476669
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0
Model Type No: boosting
RMSE: 11.2314892714055
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.625
ATE (Yes-No): -16.082 (Std.Error: 2.874)
Trimmed ATE (Yes-No): -16.272 (Std.Error: 2.953)
Upper ATE (Yes-No): -12.105 (Std.Error: 6.383)
Observational differences in treatment -3.087 (Yes-No)
treatment outcome
1: Yes 22.36419
2: No 25.45107`geom_smooth()` using method = 'loess' and formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
Outcome: 2Y. Global Tilt
Distribution:
0% 25% 50% 75% 100%
-65.2300 -12.6725 -4.0450 2.1525 20.0000
Model Type Y: boosting
RMSE: 15.4473543956142
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.625
Model Type No: boosting
RMSE: 10.9600715527255
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0
ATE (Yes-No): -14.859 (Std.Error: 2.838)
Trimmed ATE (Yes-No): -14.912 (Std.Error: 2.929)
Upper ATE (Yes-No): -13.792 (Std.Error: 9.401)
Observational differences in treatment -2.237 (Yes-No)
treatment outcome
1: Yes 24.46276
2: No 26.70024`geom_smooth()` using method = 'loess' and formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
Outcome: 6W. Lordosis (top of L1-S1)
Distribution:
0% 25% 50% 75% 100%
-65.22 -24.00 -9.38 0.00 29.00
Model Type Y: boosting
RMSE: 19.6837477587441
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.625
Model Type No: boosting
RMSE: 15.3459343912366
Params: nrounds: 50.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.75
ATE (Yes-No): -9.536 (Std.Error: 4.671)
Trimmed ATE (Yes-No): -9.628 (Std.Error: 4.886)
Upper ATE (Yes-No): -7.304 (Std.Error: 7.691)
Observational differences in treatment -3.333 (Yes-No)
treatment outcome
1: Yes -52.60333
2: No -49.27082`geom_smooth()` using method = 'loess' and formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
Outcome: 1Y. Lordosis (top of L1-S1)
Distribution:
0% 25% 50% 75% 100%
-67.87 -24.56 -7.22 0.00 23.38
Model Type Y: boosting
RMSE: 22.921217815136
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0
Model Type No: boosting
RMSE: 15.2186203445198
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0
ATE (Yes-No): -11.873 (Std.Error: 5.011)
Trimmed ATE (Yes-No): -11.907 (Std.Error: 5.147)
Upper ATE (Yes-No): -11.09 (Std.Error: 7.788)
Observational differences in treatment 0.311 (Yes-No)
treatment outcome
1: Yes -48.80097
2: No -49.11244`geom_smooth()` using method = 'loess' and formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
Outcome: 2Y. Lordosis (top of L1-S1)
Distribution:
0% 25% 50% 75% 100%
-65.5600 -22.5050 -8.1800 -0.2375 26.5200
Model Type Y: boosting
RMSE: 20.6996255387622
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0
Model Type No: boosting
RMSE: 15.1096475518204
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0
ATE (Yes-No): -10.544 (Std.Error: 5.346)
Trimmed ATE (Yes-No): -10.534 (Std.Error: 5.599)
Upper ATE (Yes-No): -10.736 (Std.Error: 5.894)
Observational differences in treatment -2.919 (Yes-No)
treatment outcome
1: Yes -51.83774
2: No -48.91858`geom_smooth()` using method = 'loess' and formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
Outcome: 6W. LGap
Distribution:
0% 25% 50% 75% 100%
-64.6206 -24.0000 -9.1254 0.2107 27.3800
Model Type Y: boosting
RMSE: 19.5656206379849
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.75
Model Type No: boosting
RMSE: 15.2814636127963
Params: nrounds: 50.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.875
ATE (Yes-No): -9.163 (Std.Error: 6.024)
Trimmed ATE (Yes-No): -9.31 (Std.Error: 6.244)
Upper ATE (Yes-No): -5.592 (Std.Error: 6.665)
Observational differences in treatment -4.195 (Yes-No)
treatment outcome
1: Yes 9.285117
2: No 13.480212`geom_smooth()` using method = 'loess' and formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
Outcome: 1Y. LGap
Distribution:
0% 25% 50% 75% 100%
-67.72420 -24.42425 -6.89620 0.58000 22.08000
Model Type Y: boosting
RMSE: 25.1082328725529
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0
Model Type No: boosting
RMSE: 15.2193675743334
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.75
ATE (Yes-No): -11.121 (Std.Error: 4.903)
Trimmed ATE (Yes-No): -11.266 (Std.Error: 5.106)
Upper ATE (Yes-No): -7.817 (Std.Error: 7.485)
Observational differences in treatment -1.258 (Yes-No)
treatment outcome
1: Yes 12.69223
2: No 13.94980`geom_smooth()` using method = 'loess' and formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
Outcome: 2Y. LGap
Distribution:
0% 25% 50% 75% 100%
-65.65180 -22.33365 -8.57860 -0.58385 25.09440
Model Type Y: boosting
RMSE: 19.1152468290633
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0
Model Type No: boosting
RMSE: 14.786678837136
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.5
ATE (Yes-No): -10.884 (Std.Error: 4.288)
Trimmed ATE (Yes-No): -11.003 (Std.Error: 4.476)
Upper ATE (Yes-No): -8.577 (Std.Error: 7.021)
Observational differences in treatment -3.781 (Yes-No)
treatment outcome
1: Yes 10.30224
2: No 14.08358`geom_smooth()` using method = 'loess' and formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
Outcome: 6W. Pelvic Tilt
Distribution:
0% 25% 50% 75% 100%
-36.4100 -8.2575 -2.0000 2.0000 14.4200
Model Type Y: boosting
RMSE: 10.4322157126939
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0
Model Type No: boosting
RMSE: 7.65983608805249
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0
ATE (Yes-No): -7.801 (Std.Error: 3.611)
Trimmed ATE (Yes-No): -7.869 (Std.Error: 3.733)
Upper ATE (Yes-No): -6.005 (Std.Error: 6.134)
Observational differences in treatment -4.452 (Yes-No)
treatment outcome
1: Yes 17.37857
2: No 21.83019`geom_smooth()` using method = 'loess' and formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
Outcome: 1Y. Pelvic Tilt
Distribution:
0% 25% 50% 75% 100%
-26.6200 -6.8975 -2.0150 1.3925 23.0000
Model Type Y: boosting
RMSE: 9.74399177099995
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.875
Model Type No: boosting
RMSE: 6.77383948826233
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.5
ATE (Yes-No): -10.338 (Std.Error: 2.405)
Trimmed ATE (Yes-No): -10.671 (Std.Error: 2.472)
Upper ATE (Yes-No): -2.743 (Std.Error: 3.919)
Observational differences in treatment -2.932 (Yes-No)
treatment outcome
1: Yes 19.57871
2: No 22.51027`geom_smooth()` using method = 'loess' and formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
Outcome: 2Y. Pelvic Tilt
Distribution:
0% 25% 50% 75% 100%
-25.630 -6.000 -1.440 2.595 12.500
Model Type Y: boosting
RMSE: 9.6160806999492
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.5
Model Type No: boosting
RMSE: 6.48592429327779
Params: nrounds: 50.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.5
ATE (Yes-No): -6.295 (Std.Error: 2.747)
Trimmed ATE (Yes-No): -6.441 (Std.Error: 2.854)
Upper ATE (Yes-No): -3.434 (Std.Error: 4.555)
Observational differences in treatment -1.502 (Yes-No)
treatment outcome
1: Yes 21.54000
2: No 23.04153`geom_smooth()` using method = 'loess' and formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'